{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data ion database\n",
    "## Get neuron list from sample\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(r\"C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis\")\n",
    "\n",
    "import IONData as IONData \n",
    "import SwcLoader\n",
    "import matplotlib\n",
    "matplotlib.use('module://matplotlib_inline.backend_inline')\n",
    "%matplotlib inline\n",
    "iondata =IONData.IONData()\n",
    "neuronlist=iondata.getNeuronListBySampleID('192106')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get neuron list by soma region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': '005.swc', 'sampleid': '202172', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': None}, {'name': '006.swc', 'sampleid': '202172', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': None}, {'name': '007.swc', 'sampleid': '202172', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': None}, {'name': '008.swc', 'sampleid': '202172', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': None}, {'name': '029.swc', 'sampleid': '202172', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': None}, {'name': '031.swc', 'sampleid': '202172', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': None}, {'name': '052.swc', 'sampleid': '210257', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '001.swc', 'sampleid': '210958', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '003.swc', 'sampleid': '210958', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '004.swc', 'sampleid': '210958', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '051.swc', 'sampleid': '210255', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '019.swc', 'sampleid': '210258', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '063.swc', 'sampleid': '210258', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '001.swc', 'sampleid': '210957', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '004.swc', 'sampleid': '210957', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '005.swc', 'sampleid': '210957', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '008.swc', 'sampleid': '210957', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '010.swc', 'sampleid': '210957', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '001.swc', 'sampleid': '210959', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '002.swc', 'sampleid': '210959', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '003.swc', 'sampleid': '210959', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '004.swc', 'sampleid': '210959', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '005.swc', 'sampleid': '210959', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '005.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '006.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '013.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '034.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '036.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '038.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '039.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '040.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '043.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '044.swc', 'sampleid': '211182', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '001.swc', 'sampleid': '211179', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '007.swc', 'sampleid': '211179', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '008.swc', 'sampleid': '211179', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '009.swc', 'sampleid': '211179', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '001.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '002.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '003.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '010.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '012.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '013.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '014.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '015.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '016.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '017.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '025.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '027.swc', 'sampleid': '211180', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '001.swc', 'sampleid': '211183', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '011.swc', 'sampleid': '211183', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '020.swc', 'sampleid': '211183', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '022.swc', 'sampleid': '211183', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '004.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '005.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '007.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '008.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '009.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '011.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '012.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '013.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '014.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '024.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '025.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '077.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '116.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '117.swc', 'sampleid': '211181', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': 'AA0433.swc', 'sampleid': '000001', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '068.swc', 'sampleid': '211889', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '001.swc', 'sampleid': '220984', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '002.swc', 'sampleid': '220984', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '003.swc', 'sampleid': '220984', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '004.swc', 'sampleid': '220984', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '005.swc', 'sampleid': '220984', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '085.swc', 'sampleid': '210258', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '049.swc', 'sampleid': '211984', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '051.swc', 'sampleid': '211984', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '065.swc', 'sampleid': '211984', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '026.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '027.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '034.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '035.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '036.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '037.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '039.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '040.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '041.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '042.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '045.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '046.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '050.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '051.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '053.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '054.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '057.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '058.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '059.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '063.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '065.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '078.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '100.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '101.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '110.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '112.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '118.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '122.swc', 'sampleid': '220985', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '008.swc', 'sampleid': '221625', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '210.swc', 'sampleid': '221624', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '033.swc', 'sampleid': '231630', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '024.swc', 'sampleid': '230050', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '025.swc', 'sampleid': '230050', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '006.swc', 'sampleid': '234158', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '009.swc', 'sampleid': '234158', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}, {'name': '055.swc', 'sampleid': '234158', 'region': 'PAG', 'type': '', 'exclude': '', 'comment': ''}]\n"
     ]
    }
   ],
   "source": [
    "CA1Neuronlist=iondata.getNeuronListBySomaRegion('PAG')\n",
    "print(CA1Neuronlist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get neuron  list by terminal or project region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# terminalPAGNeuronlist=iondata.getNeuronListByTerminalRegion('PAG')\n",
    "projectPAGNeuronlist=iondata.getNeuronListByProjectRegion('SO')\n",
    "# print(pagNeuronlist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get neuron by sample and neuron ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/192106/001.swc\n"
     ]
    }
   ],
   "source": [
    "swc = iondata.getNeuronByID('192106', '001.swc')\n",
    "neuron = SwcLoader.NeuronTree()\n",
    "neuron.readSWC(swc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get neuron properties form iondata  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/json/192106/001.swc.json\n"
     ]
    }
   ],
   "source": [
    "pro = iondata.getNeuronPropertyByID('192106', '001.swc')\n",
    "\n",
    "# print(pro)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Render region and neuron\n",
    "## Render with interactive type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/192106/011.swc\n"
     ]
    }
   ],
   "source": [
    "import Visual as nv\n",
    "\n",
    "neuronvis = nv.neuronVis()\n",
    "# neuronvis.render.setBackgroundColor((0.0,0.20,0.5,1.0))\n",
    "neuronvis.addRegion('CA2',[0.5,1.0,0.5])\n",
    "neuronvis.addNeuron('./resource/033.swc')\n",
    "\n",
    "neuronvis.render.setView('posterior')\n",
    "neuronvis.render.setLookAt((-10000,-10000,-10000),(0,0,0),(0,1,0))\n",
    "neuronvis.addNeuron('./resource/192092-012.swc',[1.0,1.0,0.0])\n",
    "neuronvis.addNeuronByID('192106','011.swc',[1.0,1.0,0.0])\n",
    "\n",
    "\n",
    "neuronvis.render.run()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Offscreen on png by pyqt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/192106/011.swc\n"
     ]
    }
   ],
   "source": [
    "# this will be black ad savepng need run on the backend of pyqt\n",
    "neuronvis = nv.neuronVis()\n",
    "# neuronvis.render.setBackgroundColor((0.0,0.20,0.5,1.0))\n",
    "neuronvis.addRegion('CA2',[0.5,1.0,0.5])\n",
    "neuronvis.addNeuron('./resource/033.swc')\n",
    "\n",
    "neuronvis.render.setView('posterior')\n",
    "neuronvis.render.setLookAt((-10000,-10000,-10000),(0,0,0),(0,1,0))\n",
    "neuronvis.addNeuron('./resource/192092-012.swc',[1.0,1.0,0.0])\n",
    "neuronvis.addNeuronByID('192106','011.swc',[1.0,1.0,0.0])\n",
    "neuronvis.render.savepng('./resource/test3.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clear scene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "neuronvis = nv.neuronVis()\n",
    "\n",
    "neuronvis.addRegion('MO')\n",
    "neuronvis.clear(True,True,False)\n",
    "neuronvis.render.run()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "# Get neuron list of 《Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain》"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <function RenderGL.__del__ at 0x000001C1FFCCD820>\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis\\RenderGL.py\", line 560, in __del__\n",
      "    glfw.terminate()\n",
      "  File \"c:\\Users\\xfwang\\.conda\\envs\\neuronVis\\lib\\site-packages\\glfw\\__init__.py\", line 826, in terminate\n",
      "    del callback_repository[window_addr]\n",
      "KeyError: -2039763536\n",
      "Exception ignored in: <function RenderGL.__del__ at 0x000001C1FFCCD820>\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis\\RenderGL.py\", line 560, in __del__\n",
      "    glfw.terminate()\n",
      "  File \"c:\\Users\\xfwang\\.conda\\envs\\neuronVis\\lib\\site-packages\\glfw\\__init__.py\", line 826, in terminate\n",
      "    del callback_repository[window_addr]\n",
      "KeyError: -2039764464\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'AA0001.swc', 'sampleid': '000001', 'region': 'SSp-m5', 'type': '', 'exclude': '', 'comment': ''}\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0001.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0002.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0003.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0004.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0005.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0006.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0007.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0008.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0009.swc\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000001/AA0010.swc\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(r\"D:/project/python/neuronVis/neuronVis\")\n",
    "import random\n",
    "import IONData \n",
    "import Visual as nv\n",
    "\n",
    "iondata =IONData.IONData()\n",
    "neuronlist=iondata.getNeuronListBySampleID('000001')\n",
    "# print(neuronlist)\n",
    "\n",
    "neuronvis = nv.neuronVis()\n",
    "# neuronvis.render.setBackgroundColor((0.0,0.20,0.5,1.0))\n",
    "print(neuronlist[0])\n",
    "neuronvis.addRegion(neuronlist[0]['region'],[0.5,1.0,0.5])\n",
    "\n",
    "neuronvis.render.setView('posterior')\n",
    "for i in range(0,10):\n",
    "    color=[random.random(),random.random(),random.random()]\n",
    "    neuronvis.addNeuronByID(neuronlist[i]['sampleid'],neuronlist[i]['name'],color)\n",
    "\n",
    "neuronvis.render.setLookAt((-10000,-10000,-10000),(0,0,0),(0,1,0))\n",
    "\n",
    "\n",
    "neuronvis.render.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'sampleid': 'sample', 'name': 'AA0916.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0915.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0914.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0897.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0884.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0873.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0866.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0865.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0859.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0803.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0802.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0793.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0782.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0738.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0672.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0670.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0666.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0659.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0658.swc', 'region': 'MOp2/3'}, {'sampleid': 'sample', 'name': 'AA0639.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0622.swc', 'region': 'MOp2/3'}, {'sampleid': 'sample', 'name': 'AA0597.swc', 'region': 'MOp2/3'}, {'sampleid': 'sample', 'name': 'AA0592.swc', 'region': 'MOp2/3'}, {'sampleid': 'sample', 'name': 'AA0582.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0475.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0474.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0471.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0467.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0450.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0446.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0439.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0426.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0424.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0419.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0418.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0416.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0409.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0407.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0402.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0395.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0329.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0291.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0284.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0241.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0238.swc', 'region': 'ACAd2/3'}, {'sampleid': 'sample', 'name': 'AA0237.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0232.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0118.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0116.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0014.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0013.swc', 'region': 'MOs2/3'}, {'sampleid': 'sample', 'name': 'AA0009.swc', 'region': 'MOp2/3'}, {'sampleid': 'sample', 'name': 'AA0007.swc', 'region': 'SSp-ul2/3'}, {'sampleid': 'sample', 'name': 'AA0006.swc', 'region': 'MOp2/3'}, {'sampleid': 'sample', 'name': 'AA0003.swc', 'region': 'MOp2/3'}]\n",
      "[{'sampleid': 'sample', 'name': 'AA0913.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0907.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0906.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0905.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0889.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0888.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0887.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0883.swc', 'region': 'ACAd5'}, {'sampleid': 'sample', 'name': 'AA0880.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0874.swc', 'region': 'ACAd5'}, {'sampleid': 'sample', 'name': 'AA0868.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0867.swc', 'region': 'ACAd5'}, {'sampleid': 'sample', 'name': 'AA0858.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0853.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0842.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0841.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0840.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0798.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0797.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0790.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0789.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0786.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0774.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0773.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0771.swc', 'region': 'ACAd5'}, {'sampleid': 'sample', 'name': 'AA0767.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0749.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0746.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0745.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0744.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0743.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0742.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0735.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0734.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0669.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0668.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0664.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0656.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0646.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0644.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0632.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0600.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0588.swc', 'region': 'MOp5'}, {'sampleid': 'sample', 'name': 'AA0578.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0575.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0556.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 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{'sampleid': 'sample', 'name': 'AA0400.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0397.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0396.swc', 'region': 'FRP5'}, {'sampleid': 'sample', 'name': 'AA0332.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0327.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0324.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0300.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0289.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0288.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0287.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0286.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0285.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0281.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0279.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0276.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0274.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0272.swc', 'region': 'MOp5'}, {'sampleid': 'sample', 'name': 'AA0271.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0269.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0267.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0265.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0236.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0235.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0233.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0230.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0190.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0184.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0130.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0112.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0108.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0106.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0102.swc', 'region': 'MOp5'}, {'sampleid': 'sample', 'name': 'AA0100.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0099.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0065.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0064.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0059.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0046.swc', 'region': 'MOp5'}, {'sampleid': 'sample', 'name': 'AA0037.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0036.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0035.swc', 'region': 'MOp5'}, {'sampleid': 'sample', 'name': 'AA0034.swc', 'region': 'MOp5'}, {'sampleid': 'sample', 'name': 'AA0010.swc', 'region': 'MOs5'}, {'sampleid': 'sample', 'name': 'AA0004.swc', 'region': 'MOp5'}, {'sampleid': 'sample', 'name': 'AA0002.swc', 'region': 'MOp5'}]\n",
      "[{'sampleid': 'sample', 'name': 'AA0911.swc', 'region': 'FRP6a'}, {'sampleid': 'sample', 'name': 'AA0908.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0900.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0876.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0854.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0784.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0781.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0775.swc', 'region': 'ACAd6a'}, {'sampleid': 'sample', 'name': 'AA0748.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0747.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0741.swc', 'region': 'ORBl6a'}, {'sampleid': 'sample', 'name': 'AA0739.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0733.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0650.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0602.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0553.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0549.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0543.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0541.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0473.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0464.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0463.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0461.swc', 'region': 'FRP6a'}, {'sampleid': 'sample', 'name': 'AA0457.swc', 'region': 'PL6a'}, {'sampleid': 'sample', 'name': 'AA0410.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0408.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0406.swc', 'region': 'ORBvl6a'}, {'sampleid': 'sample', 'name': 'AA0404.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0401.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0333.swc', 'region': 'FRP6a'}, {'sampleid': 'sample', 'name': 'AA0323.swc', 'region': 'ORBl6a'}, {'sampleid': 'sample', 'name': 'AA0320.swc', 'region': 'ORBl6a'}, {'sampleid': 'sample', 'name': 'AA0280.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0243.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0240.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0239.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0231.swc', 'region': 'ACAd6a'}, {'sampleid': 'sample', 'name': 'AA0225.swc', 'region': 'MOp6a'}, {'sampleid': 'sample', 'name': 'AA0224.swc', 'region': 'MOp6a'}, {'sampleid': 'sample', 'name': 'AA0140.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0113.swc', 'region': 'ACAd6a'}, {'sampleid': 'sample', 'name': 'AA0111.swc', 'region': 'ACAd6a'}, {'sampleid': 'sample', 'name': 'AA0110.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0107.swc', 'region': 'MOp6a'}, {'sampleid': 'sample', 'name': 'AA0062.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0060.swc', 'region': 'MOp6a'}, {'sampleid': 'sample', 'name': 'AA0044.swc', 'region': 'MOs6a'}, {'sampleid': 'sample', 'name': 'AA0042.swc', 'region': 'MOp6a'}, {'sampleid': 'sample', 'name': 'AA0040.swc', 'region': 'MOp6a'}, {'sampleid': 'sample', 'name': 'AA0005.swc', 'region': 'MOp6a'}]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0911.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0908.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0900.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0876.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0854.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0784.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0781.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0775.swc.json\n",
      "unknow\n",
      "unknow\n",
      "unknow\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0748.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0747.swc.json\n",
      "fibertracts\n",
      "unknow\n",
      "fibertracts\n",
      "unknow\n",
      "fibertracts\n",
      "unknow\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0741.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0739.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0733.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0650.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0602.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0553.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0549.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0543.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0541.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0473.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0464.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0463.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0461.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0457.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0410.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0408.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0406.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0404.swc.json\n",
      "unknow\n",
      "unknow\n",
      "unknow\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0401.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0333.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0323.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0320.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0280.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0243.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0240.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0239.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0231.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0225.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0224.swc.json\n",
      "unknow\n",
      "unknow\n",
      "unknow\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0140.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0113.swc.json\n",
      "unknow\n",
      "unknow\n",
      "unknow\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0111.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0110.swc.json\n",
      "unknow\n",
      "unknow\n",
      "unknow\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0107.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0062.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0060.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0044.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0042.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0040.swc.json\n",
      "exist  G:\\workspace\\neuron-vis/neuronVis/../resource/json/sample/AA0005.swc.json\n",
      "              sample-AA0911.swc  sample-AA0908.swc  sample-AA0900.swc  \\\n",
      "Total                  7.595736           5.769359           5.694605   \n",
      "Cortex                 6.999596           5.769359           4.180586   \n",
      "CortexIpsi             6.290374           5.769359           4.180586   \n",
      "CortexContra           5.663767           0.000000           0.000000   \n",
      "Motor                  6.197560           4.921392           2.660940   \n",
      "Other                  5.796277           4.657716           3.679092   \n",
      "STR                    5.774260           0.000000           4.972619   \n",
      "STRIpsi                4.866439           0.000000           4.972619   \n",
      "STRContra              4.731155           0.000000           0.000000   \n",
      "\n",
      "              sample-AA0876.swc  sample-AA0854.swc  sample-AA0784.swc  \\\n",
      "Total                  7.154874           5.487525           6.242491   \n",
      "Cortex                 6.782176           1.786757           2.376554   \n",
      "CortexIpsi             6.043382           1.786757           2.376554   \n",
      "CortexContra           5.495328           0.000000           0.000000   \n",
      "Motor                  6.503129           1.786757           2.240226   \n",
      "Other                  4.347390           0.000000           0.554087   \n",
      "STR                    4.620105           1.606934           1.794631   \n",
      "STRIpsi                3.756448           1.606934           1.794631   \n",
      "STRContra              3.594209           0.000000           0.000000   \n",
      "\n",
      "              sample-AA0781.swc  sample-AA0775.swc  sample-AA0748.swc  \\\n",
      "Total                  5.647325           6.783310           6.929938   \n",
      "Cortex                 3.685187           6.695353           6.673097   \n",
      "CortexIpsi             3.685187           6.690683           5.821910   \n",
      "CortexContra           0.000000           0.416735           5.538528   \n",
      "Motor                  3.593695           5.909185           6.182150   \n",
      "Other                  0.840304           5.476980           4.927652   \n",
      "STR                    1.599364           0.000000           3.455383   \n",
      "STRIpsi                1.599364           0.000000           0.000000   \n",
      "STRContra              0.000000           0.000000           3.455383   \n",
      "\n",
      "              sample-AA0747.swc  ...  sample-AA0113.swc  sample-AA0111.swc  \\\n",
      "Total                  6.125065  ...           7.454933           5.029166   \n",
      "Cortex                 5.453876  ...           7.304032           5.029166   \n",
      "CortexIpsi             5.453876  ...           7.174482           5.029166   \n",
      "CortexContra           0.000000  ...           3.865123           0.000000   \n",
      "Motor                  4.626160  ...           6.198558           2.273751   \n",
      "Other                  4.331680  ...           6.419274           4.848885   \n",
      "STR                    3.067237  ...           1.263989           0.000000   \n",
      "STRIpsi                3.067237  ...           1.263989           0.000000   \n",
      "STRContra              0.000000  ...           0.000000           0.000000   \n",
      "\n",
      "              sample-AA0110.swc  sample-AA0107.swc  sample-AA0062.swc  \\\n",
      "Total                  5.978973           5.826698           7.108987   \n",
      "Cortex                 5.895326           5.627942           6.717794   \n",
      "CortexIpsi             5.891477           5.004869           5.577925   \n",
      "CortexContra           0.212361           4.197181           5.870170   \n",
      "Motor                  5.206838           4.817462           5.803483   \n",
      "Other                  4.559981           4.476033           5.655599   \n",
      "STR                    0.000000           0.811201           4.291174   \n",
      "STRIpsi                0.000000           0.518564           0.255022   \n",
      "STRContra              0.000000           0.402877           4.276855   \n",
      "\n",
      "              sample-AA0060.swc  sample-AA0044.swc  sample-AA0042.swc  \\\n",
      "Total                  5.612360           4.203400           7.610227   \n",
      "Cortex                 5.612360           4.203400           7.412526   \n",
      "CortexIpsi             5.612360           4.203400           6.846121   \n",
      "CortexContra           0.000000           0.000000           5.815562   \n",
      "Motor                  5.246744           4.179558           6.843187   \n",
      "Other                  3.579107           0.380666           5.821538   \n",
      "STR                    0.000000           0.000000           3.897057   \n",
      "STRIpsi                0.000000           0.000000           0.238832   \n",
      "STRContra              0.000000           0.000000           3.879517   \n",
      "\n",
      "              sample-AA0040.swc  sample-AA0005.swc  \n",
      "Total                  4.416375           7.262244  \n",
      "Cortex                 4.415311           7.046386  \n",
      "CortexIpsi             4.415311           6.764770  \n",
      "CortexContra           0.000000           4.611170  \n",
      "Motor                  4.375747           6.378783  \n",
      "Other                  0.657362           5.642895  \n",
      "STR                    0.000000           3.026302  \n",
      "STRIpsi                0.000000           0.908685  \n",
      "STRContra              0.000000           2.861926  \n",
      "\n",
      "[9 rows x 50 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys,copy,os\n",
    "sys.path.append(os.getcwd()+\"/neuronVis\")\n",
    "import json\n",
    "\n",
    "import IONData as IONData \n",
    "import BrainRegion as BR \n",
    "import Cluster\n",
    "import Visual as nv\n",
    "\n",
    "import numpy as np\n",
    "from scipy.spatial import Delaunay\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn\tas sns\n",
    "f=open('./resource/paper1figure5list.json', encoding='gbk')\n",
    "paper1figure5list=[]\n",
    "paper1figure5list = json.load(f)\n",
    "# print(paper1figure5list)\n",
    "\n",
    "layer2_3list=[]\n",
    "layer5list=[]\n",
    "layer6list=[]\n",
    "for neuron in paper1figure5list:\n",
    "\tif '2/3' in neuron['region']:\n",
    "\t\tlayer2_3list.append(neuron)\n",
    "\tif '5' in neuron['region']:\n",
    "\t\tlayer5list.append(neuron)\n",
    "\tif '6' in neuron['region']:\n",
    "\t\tlayer6list.append(neuron)\n",
    "\n",
    "print((layer2_3list))\n",
    "print((layer5list))\n",
    "print((layer6list))\n",
    "\n",
    "br = BR.BrainRegion()\n",
    "br.praseJson()\n",
    "\n",
    "df = pd.DataFrame()\n",
    "\n",
    "iondata=IONData.IONData()\n",
    "\n",
    "for neuron in layer6list:\n",
    "\tbrproperty=BR.RegionProperty(copy.deepcopy(br))\n",
    "\tbrpropertyLeft=BR.RegionProperty(copy.deepcopy(br))\n",
    "\tbrpropertyRight=BR.RegionProperty(copy.deepcopy(br))\n",
    "\tprop=[]\n",
    "\tproperty = iondata.getNeuronPropertyByID(neuron['sampleid'], neuron['name'])\n",
    "\tbrproperty.setProperty(property['projectregion'])\n",
    "\tbrpropertyLeft.setProperty(property['projectleftregion'])\n",
    "\tbrpropertyRight.setProperty(property['projectrightregion'])\n",
    "\n",
    "\tprop.append(property['axonlength'])\n",
    "\n",
    "\t#cortex length\n",
    "\tctxlist = brproperty.getRegionPropertyList('CTX')\n",
    "\tctxsum=0\n",
    "\tfor subregion in ctxlist:\n",
    "\t\tctxsum=ctxsum+subregion[2]\n",
    "\tprop.append(ctxsum)\n",
    "\n",
    "\t#ipsi contra cortex length\n",
    "\tleftctxlist = brpropertyLeft.getRegionPropertyList('CTX')\n",
    "\trightctxlist = brpropertyRight.getRegionPropertyList('CTX')\n",
    "\tleftctxsum=0\n",
    "\tfor subregion in leftctxlist:\n",
    "\t\tleftctxsum=leftctxsum+subregion[2]\n",
    "\trightctxsum=0\n",
    "\tfor subregion in rightctxlist:\n",
    "\t\trightctxsum=rightctxsum+subregion[2]\n",
    "\tipsi=0\n",
    "\tcontra=0\n",
    "\tif property['somapoint'][2]<5700:\n",
    "\t\tipsi=leftctxsum\n",
    "\t\tcontra=rightctxsum\n",
    "\telse:\n",
    "\t\tipsi=rightctxsum\n",
    "\t\tcontra=leftctxsum\n",
    "\tprop.append(ipsi)\n",
    "\tprop.append(contra)\n",
    "\n",
    "\n",
    "\t#motor cortex length\n",
    "\tmolist = brproperty.getRegionPropertyList('MO')\n",
    "\tmosum=0\n",
    "\tfor subregion in molist:\n",
    "\t\tmosum=mosum+subregion[2]\n",
    "\tprop.append(mosum)\n",
    "\n",
    "\t#other cortex length\n",
    "\tprop.append(ctxsum-mosum)\n",
    "\n",
    "\t# STR\n",
    "\tstrlist = brproperty.getRegionPropertyList('STR')\n",
    "\tstrsum=0\n",
    "\tfor subregion in strlist:\n",
    "\t\tstrsum=strsum+subregion[2]\n",
    "\tprop.append(strsum)\n",
    "\n",
    "\t#ipsi contra str length\n",
    "\tleftstrlist = brpropertyLeft.getRegionPropertyList('STR')\n",
    "\trightstrlist = brpropertyRight.getRegionPropertyList('STR')\n",
    "\tleftctxsum=0\n",
    "\tfor subregion in leftstrlist:\n",
    "\t\tleftctxsum=leftctxsum+subregion[2]\n",
    "\trightctxsum=0\n",
    "\tfor subregion in rightstrlist:\n",
    "\t\trightctxsum=rightctxsum+subregion[2]\n",
    "\tipsi=0\n",
    "\tcontra=0\n",
    "\tif property['somapoint'][2]<5700:\n",
    "\t\tipsi=leftctxsum\n",
    "\t\tcontra=rightctxsum\n",
    "\telse:\n",
    "\t\tipsi=rightctxsum\n",
    "\t\tcontra=leftctxsum\n",
    "\tprop.append(ipsi)\n",
    "\tprop.append(contra)\n",
    "\n",
    "\tdf[neuron['sampleid']+\"-\"+neuron['name']]=prop\n",
    "\n",
    "\n",
    "\n",
    "df.set_index(pd.Index(['Total', 'Cortex', 'CortexIpsi', 'CortexContra','Motor','Other','STR','STRIpsi','STRContra']),inplace=True, drop = True)\n",
    "df=(np.log2(df/1000.0+1))\n",
    "print(df)\n",
    "\n",
    "# cluster= Cluster.Cluster()\n",
    "# cluster.fclusterDF(df)\n",
    "# print(cluster.cluster)\n",
    "# plt.show()\n",
    "\n",
    "cmap= sns.dark_palette('black',reverse=True,n_colors=50)\n",
    "# cmap= sns.dark_palette('blue',reverse=True,n_colors=50)\n",
    "cmap2=sns.dark_palette('blue',reverse=False,n_colors=25)\n",
    "cmap3=sns.dark_palette('blue',reverse=True,n_colors=25)\n",
    "cmap4=sns.dark_palette('yellow',reverse=False,n_colors=50)\n",
    "cmap.extend(cmap2)\n",
    "cmap.extend(cmap3)\n",
    "cmap.extend(cmap4)\n",
    "# cmap.append(np.array([.3,.7,.6,1]))\n",
    "# cmap.insert(0,np.array([.7,.7,.5,1]))\n",
    "\n",
    "# sns.heatmap(df,cmap=cmap)\n",
    "\n",
    "\n",
    "sns.clustermap(df, fmt=\"d\", cmap=cmap,xticklabels=True,yticklabels=True,row_cluster=False)\n",
    "plt.show()\n",
    "\n",
    "## render\n",
    "# neuronvis = nv.neuronVis()\n",
    "# neuronvis.addNeuronByList(layer6list[1:3])\n",
    "# # neuronvis.addNeuronByID(paper1figure5list[100]['sampleid'],paper1figure5list[100]['name'])\n",
    "# # neuronvis.render.setView('posterior')\n",
    "# neuronvis.render.setLookAt((-10000,-10000,-10000),(0,0,0),(0,1,0))\n",
    "# neuronvis.render\n",
    "\n",
    "# # # neuronvis.render.savepng('./resource/test2.png')\n",
    "# nv.app.run()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Render neuron to svg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/192106/033.swc\n"
     ]
    }
   ],
   "source": [
    "import sys,copy,os\n",
    "sys.path.append(os.getcwd()+\"/neuronVis\")\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import IONData \n",
    "import SwcLoader\n",
    "iondata =IONData.IONData()\n",
    "\n",
    "swc = iondata.getNeuronByID('192106', '033.swc')\n",
    "\n",
    "neuron = SwcLoader.NeuronTree()\n",
    "neuron.readSWC(swc)\n",
    "plt.figure(figsize=(12, 12))\n",
    "axes0 = plt.subplot(111)\n",
    "plt.axis('off')\n",
    "axes0.set_xlim(0, 10000)\n",
    "axes0.set_ylim(0, 10000)\n",
    "# plt.Circle((neuron.root.z, neuron.root.y), 300)\n",
    "for edge in neuron.edges:\n",
    "    x=[]\n",
    "    y=[]\n",
    "    for p in edge.data:\n",
    "        x.append(10000-p.z)\n",
    "        y.append(10000-p.y)\n",
    "    plt.plot(x, y,color='#FFDD44')\n",
    "# plt.figure(facecolor='gainsboro')\n",
    "plt.plot(10000-neuron.root.z,10000- neuron.root.y,'ob')\n",
    "\n",
    "\n",
    "plt.savefig(fname=\"./resource/neuron.svg\",format=\"svg\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Render soma on flatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/200313/082.swc\n",
      "[217.591455078125, 209.5662841796875, 86.1344970703125] [521, 473]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000002/1060693821_17787_5268-X6865-Y9780_reg.swc\n",
      "[320.1875, 169.2375, 102.22899780273437] [501, 752]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000002/1060696070_18867_4218-X8269-Y11914_reg.swc\n",
      "[251.593994140625, 194.6324951171875, 94.98250122070313] [502, 552]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/000002/1060696562_18869_5019-X5442-Y5910_reg.swc\n",
      "[316.0135009765625, 153.7864990234375, 95.17899780273437] [527, 751]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/212071/004.swc\n",
      "[258.54150390625, 209.4544921875, 85.414013671875] [450, 556]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/212073/028.swc\n",
      "[297.14599609375, 202.5239990234375, 85.372021484375] [417, 681]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/220195/204.swc\n",
      "[229.372509765625, 190.6989990234375, 83.87750244140625] [525, 525]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/220237/016.swc\n",
      "[331.2090087890625, 187.09100341796875, 91.02451171875] [445, 782]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/220238/053.swc\n",
      "[321.5570068359375, 204.58900146484376, 84.275] [396, 763]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/220198/322.swc\n",
      "[264.1264892578125, 213.7114990234375, 56.114990234375] [412, 602]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/220198/436.swc\n",
      "[296.1860107421875, 209.5510009765625, 45.925] [385, 705]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/212075/060.swc\n",
      "[282.5530029296875, 183.293994140625, 50.32001953125] [447, 680]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221066/014.swc\n",
      "[314.9580078125, 173.106005859375, 70.57899780273438] [465, 755]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221067/058.swc\n",
      "[321.460009765625, 155.35, 61.01500244140625] [494, 781]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221088/043.swc\n",
      "[312.11201171875, 181.13399658203124, 66.125] [444, 750]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221478/690.swc\n",
      "[347.8169921875, 119.2719970703125, 83.48099975585937] [575, 834]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221224/029.swc\n",
      "[388.9080078125, 102.656005859375, 92.28251953125] [622, 905]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221243/113.swc\n",
      "[305.2050048828125, 183.431005859375, 45.364990234375] [433, 740]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221518/062.swc\n",
      "[338.731005859375, 127.51199951171876, 67.1] [548, 821]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221456/045.swc\n",
      "[323.32900390625, 137.7699951171875, 82.19099731445313] [545, 779]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221456/067.swc\n",
      "[339.4070068359375, 169.63199462890626, 64.4] [465, 818]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221480/095.swc\n",
      "[366.7469970703125, 96.88800048828125, 88.71400146484375] [612, 871]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221586/100.swc\n",
      "[319.1580078125, 184.04300537109376, 63.339990234375] [434, 766]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221509/087.swc\n",
      "[340.7889892578125, 177.043994140625, 52.835009765625] [444, 831]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221706/478.swc\n",
      "[386.2219970703125, 91.1155029296875, 89.943017578125] [633, 901]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221644/006.swc\n",
      "[245.431005859375, 184.6050048828125, 66.72999877929688] [499, 578]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221644/026.swc\n",
      "[257.397998046875, 167.09599609375, 78.0969970703125] [525, 611]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221756/034.swc\n",
      "[294.4330078125, 211.6639892578125, 52.12998046875] [383, 695]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221735/010.swc\n",
      "[302.3669921875, 151.56400146484376, 81.089013671875] [519, 727]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221525/006.swc\n",
      "[269.1340087890625, 171.44200439453124, 87.598486328125] [516, 630]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221727/034.swc\n",
      "[277.8840087890625, 156.31400146484376, 68.12001953125] [514, 676]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/230106/005.swc\n",
      "[335.8360107421875, 115.19300537109375, 68.97998046875] [568, 817]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221481/360.swc\n",
      "[340.74599609375, 127.71700439453124, 69.8] [552, 823]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221481/451.swc\n",
      "[283.4860107421875, 178.2260009765625, 78.6489990234375] [478, 667]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/230070/022.swc\n",
      "[326.92099609375, 111.58699951171874, 68.82000122070312] [574, 801]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221642/549.swc\n",
      "[298.2800048828125, 150.04000244140624, 61.635009765625] [509, 729]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221730/040.swc\n",
      "[364.6429931640625, 101.23200073242188, 68.93499755859375] [589, 872]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221731/054.swc\n",
      "[328.55, 119.46300048828125, 66.47998046875] [562, 803]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221663/058.swc\n",
      "[339.0679931640625, 178.26099853515626, 81.27548828125] [458, 808]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221663/083.swc\n",
      "[329.6669921875, 164.53299560546876, 84.8375] [492, 785]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221521/037.swc\n",
      "[339.0340087890625, 169.98599853515626, 43.57999877929687] [455, 829]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/231686/009.swc\n",
      "[293.1320068359375, 200.34300537109374, 65.9] [413, 690]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221242/072.swc\n",
      "[270.1580078125, 205.5639892578125, 63.789990234375] [424, 618]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/221086/162.swc\n",
      "[295.2510009765625, 189.6030029296875, 71.11500244140625] [437, 695]\n",
      "write  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/233284/135.swc\n",
      "[280.9909912109375, 189.41099853515624, 91.6384765625] [470, 641]\n",
      "exist  C:\\\\Users\\\\xfwang\\\\Documents\\\\workspace\\\\neuron-vis\\\\neuronVis/../resource/swc/233285/127.swc\n",
      "[397.2050048828125, 104.1989990234375, 106.7490234375] [643, 911]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c2b6e4e880>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage import draw, io\n",
    "import Flatmap\n",
    "import nrrd\n",
    "iondata.getFileFromServer(\"outpx.nrrd\")\n",
    "iondata.getFileFromServer(\"outpy.nrrd\")\n",
    "outpx,outpxHeader = nrrd.read(\"./resource/outpx.nrrd\",index_order='F')\n",
    "outpy,outpyHeader = nrrd.read(\"./resource/outpy.nrrd\",index_order='F')\n",
    "# %%\n",
    "\n",
    "flatmapim = io.imread(\"./resource/flatmapedge.png\")\n",
    "x=[]\n",
    "y=[]\n",
    "c=[]\n",
    "CA1Neuronlist=iondata.getNeuronListBySomaRegion('SSs')\n",
    "\n",
    "for neuron in CA1Neuronlist[::20]:\n",
    "    tree = iondata.getNeuronTreeByID(neuron['sampleid'], neuron['name'])\n",
    "    neuron['xyz']=tree.root.xyz\n",
    "    neuron['tree']=tree\n",
    "    point = [neuron['xyz'][0],neuron['xyz'][1],11400-neuron['xyz'][2] if neuron['xyz'][2]>5700 else neuron['xyz'][2]]\n",
    "    point=[point[0]/20,point[1]/20,point[2]/20]\n",
    "        # p2d = map2Flatmap(flatenPara,np.array(p[1])*2,True)\n",
    "    p2d= Flatmap.map2FlatmapIndex(None,np.array(point)*2,outpx,outpy)\n",
    "    neuron['p2d']=p2d\n",
    "    print(point,p2d)\n",
    "    color = [1,0,0]\n",
    "\n",
    "    x.append(p2d[0])\n",
    "    y.append(p2d[1])\n",
    "    c.append(color)\n",
    "    neuron['color']=color\n",
    "plt.figure()\n",
    "plt.scatter(x,y,s=1,c=c)\n",
    "plt.imshow(flatmapim)\n",
    "# plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UpSample the neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  ./neuronVis/../resource/swc/000001/AA0002.swc\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(r\"./neuronVis\")\n",
    "import IONData\n",
    "import SwcLoader\n",
    "import Render\n",
    "import GeometryAdapter\n",
    "import BrainRegion\n",
    "import NeuronProcess\n",
    "from vispy import app\n",
    "iondata=IONData.IONData()\n",
    "swc = iondata.getNeuronByID('000001', 'AA0002.swc')\n",
    "neuron = SwcLoader.NeuronTree()\n",
    "neuron.readSWC(swc)\n",
    "newneuron=NeuronProcess.UpSampleFromNeuronTree(neuron,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xfwang\\Documents\\workspace\\neuron-vis\\neuronVis\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import sys\n",
    "import copy\n",
    "import os\n",
    "sys.path.append(\".\\\\neuronVis\")\n",
    "import json\n",
    "import IONData\n",
    "import Flatmap\n",
    "import BoundLaplace\n",
    "import nrrd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "matplotlib.use('module://matplotlib_inline.backend_inline')\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "layer = [120, 400, 570, 850, 1200] # in micron, SS results employing ../neuronVis/LayerThickness.py\n",
    "layerName = ['L1','L2/3','L4','L5','L6']\n",
    "\n",
    "iondata = IONData.IONData()\n",
    "\n",
    "flatenPara=Flatmap.createSurfaceGraph()\n",
    "\n",
    "res,gridpath = iondata.getFileFromServer(\"boundlaplace20.nrrd\")\n",
    "grid,header = nrrd.read(gridpath)\n",
    "\n",
    "resRelaxation,RelaxationPath=iondata.getFileFromServer('boundlaplaceout20.nrrd')\n",
    "relaxation,relaxationheader = nrrd.read(RelaxationPath)\n",
    "\n",
    "resdv0,dv0Path=iondata.getFileFromServer('dv0.nrrd')\n",
    "dv0,dv0header = nrrd.read(dv0Path)\n",
    "resdv1,dv1Path=iondata.getFileFromServer('dv1.nrrd')\n",
    "dv1,dv1header = nrrd.read(dv1Path)\n",
    "resdv2,dv2Path=iondata.getFileFromServer('dv2.nrrd')\n",
    "dv2,dv2header = nrrd.read(dv2Path)\n",
    "\n",
    "dv0=dv0.astype(np.float32)/1000-1\n",
    "dv1=dv1.astype(np.float32)/1000-1\n",
    "dv2=dv2.astype(np.float32)/1000-1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  .\\neuronVis/../resource/swc/000002/1060693894_18452_5511-X26566-Y3403_reg.swc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import FlatNeuron\n",
    "import IONData\n",
    "iondata = IONData.IONData()\n",
    "\n",
    "\n",
    "neurontree = iondata.getNeuronTreeByID(\"000002\",\"1060693894_18452_5511-X26566-Y3403_reg.swc\")\n",
    "FlatNeuron.flatNeuronDepth(neurontree,grid,dv0,dv1,dv2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "373.333px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
